National University of Defense Technology

About: National University of Defense Technology is a education organization based out in Changsha, China. It is known for research contribution in the topics: Computer science & Radar. The organization has 39430 authors who have published 40181 publications receiving 358979 citations. The organization is also known as: Guófáng Kēxuéjìshù Dàxué & NUDT.

Energy-Efficient Multi-UAV-Enabled Multiaccess Edge Computing Incorporating NOMA

Abstract: Multiaccess edge computing (MEC) is regarded as a promising solution to overcome the limit on the computation capacity of mobile devices. This article investigates an energy-efficient unmanned aerial vehicle (UAV)-enabled MEC framework incorporating nonorthogonal multiple access (NOMA), where multiple UAVs are deployed as edge servers to provide computation assistance to terrestrial users and NOMA is adopted to reduce the energy consumption of task offloading. A utility is formed to mathematically evaluate the weighted energy cost of the system. Due to the coupling of parameters, the minimization of utility is a highly nonconvex problem and therefore, the problem is decomposed into two more tractable subproblems, i.e., the optimal allocation of radio and computation resources given UAV trajectories, and the trajectory planning based on given resource allocation schemes. These two problems are converted to convex ones via successive convex approximation (SCA) and quadratic approximation, respectively. Then, an efficient iterative algorithm is proposed where these two subproblems are alternately solved to gradually approach the optimal resource management of the proposed system. Sufficient numerical results show that our proposed strategy has a remarkable advantage over existing systems in terms of energy efficiency.

Stable laser-ion acceleration in the light sail regime

Abstract: We present experimental results on ion acceleration with circularly polarized, ultrahigh contrast laser pulses focused to peak intensities of $5\ifmmode\times\else\texttimes\fi{}{10}^{19}\text{ }\text{ }\mathrm{W}\text{ }{\mathrm{cm}}^{\ensuremath{-}2}$ onto polymer targets of a few 10 nanometer thickness. We observed spatially and energetically separated protons and carbon ions that accumulate to pronounced peaks around 2 MeV containing as much as 6.5% of the laser energy. Based on particle-in-cell simulation, we illustrate that an early separation of heavier carbon ions and lighter protons creates a stable interface that is maintained beyond the end of the radiation pressure dominated acceleration process.

Distributed Multiagent Coordinated Learning for Autonomous Driving in Highways Based on Dynamic Coordination Graphs

Abstract: Autonomous driving is one of the most important AI applications and has attracted extensive interest in recent years. A large number of studies have successfully applied reinforcement learning techniques in various aspects of autonomous driving, ranging from low-level control of driving maneuvers to higher level of strategic decision-making. However, comparatively less progress has been made in investigating how co-existing autonomous vehicles would interact with each other in a common environment and how reinforcement learning can be helpful in such situations by applying multiagent reinforcement learning techniques in the high-level strategic decision-making of the following or overtaking for a group of autonomous vehicles in highway scenarios. Learning to achieve coordination among vehicles in such situations is challenging due to the unique feature of vehicular mobility, which renders it infeasible to directly apply the existing coordinated learning approaches. To solve this problem, we propose using dynamic coordination graph to model the continuously changing topology during vehicles’ interactions and come up with two basic learning approaches to coordinate the driving maneuvers for a group of vehicles. Several extension mechanisms are then presented to make these approaches workable in a more complex and realistic setting with any number of vehicles. The experimental evaluation has verified the benefits of the proposed coordinated learning approaches, compared with other approaches that learn without coordination or rely on some traditional mobility models based on some expert driving rules.

Analysis of Inter-Satellite Link Paths for LEO Mega-Constellation Networks

Abstract: Mega-constellation networks (MCNs) based on low earth orbit (LEO) satellites have become increasingly important with many projects in the design or implementation phase. For those MCN systems with inter-satellite links (ISLs), the large number of satellites increases the routing complexity and the required hop-count of ISL paths. This paper aims to provide insights into the topology and routing design in MCNs through the analysis of ISL paths. We propose a theoretical model to explicitly estimate the ISL hop-count between ground users in MCNs with inclined orbits. The spatial distribution properties of hop-count are derived based on the proposed method. Based on an exemplary constellation, that is Starlink, the global distribution patterns of hop-count and the path difference caused by different access satellites are illustrated for the first time. In Starlink, the difference of the paths from different starting satellites can be up to 45 hops. The numerical results show that optimizing the constellation phasing factor can effectively reduce the average hop-count, for both the whole network and specific regional users.